Construct aesthetic mappings

Aesthetic mappings describe how variables in the data are mapped to visual properties (aesthetics) of geoms. Aesthetic mappings can be set in ggplot() and in individual layers.

Usage

aes(x, y, ...)

Arguments

x, y, ...

<data-masking> List of name-value pairs in the form aesthetic = variable describing which variables in the layer data should be mapped to which aesthetics used by the paired geom/stat. The expression variable is evaluated within the layer data, so there is no need to refer to the original dataset (i.e., use ggplot(df, aes(variable)) instead of ggplot(df, aes(df$variable))). The names for x and y aesthetics are typically omitted because they are so common; all other aesthetics must be named.

Value

A list with class uneval. Components of the list are either quosures or constants.

Details

This function also standardises aesthetic names by converting color to colour (also in substrings, e.g., point_color to point_colour) and translating old style R names to ggplot names (e.g., pch to shape and cex to size).

Quasiquotation

aes() is a quoting function. This means that its inputs are quoted to be evaluated in the context of the data. This makes it easy to work with variables from the data frame because you can name those directly. The flip side is that you have to use quasiquotation to program with aes(). See a tidy evaluation tutorial such as the dplyr programming vignette to learn more about these techniques.

See also

vars() for another quoting function designed for faceting specifications.

Run vignette("ggplot2-specs") to see an overview of other aesthetics that can be modified.

Delayed evaluation for working with computed variables.

Other aesthetics documentation: aes_colour_fill_alpha, aes_group_order, aes_linetype_size_shape, aes_position

Examples

aes(x = mpg, y = wt)
#> Aesthetic mapping: 
#> * `x` -> `mpg`
#> * `y` -> `wt`
aes(mpg, wt)
#> Aesthetic mapping: 
#> * `x` -> `mpg`
#> * `y` -> `wt`

# You can also map aesthetics to functions of variables
aes(x = mpg ^ 2, y = wt / cyl)
#> Aesthetic mapping: 
#> * `x` -> `mpg^2`
#> * `y` -> `wt/cyl`

# Or to constants
aes(x = 1, colour = "smooth")
#> Aesthetic mapping: 
#> * `x`      -> 1
#> * `colour` -> "smooth"

# Aesthetic names are automatically standardised
aes(col = x)
#> Aesthetic mapping: 
#> * `colour` -> `x`
aes(fg = x)
#> Aesthetic mapping: 
#> * `colour` -> `x`
aes(color = x)
#> Aesthetic mapping: 
#> * `colour` -> `x`
aes(colour = x)
#> Aesthetic mapping: 
#> * `colour` -> `x`

# aes() is passed to either ggplot() or specific layer. Aesthetics supplied
# to ggplot() are used as defaults for every layer.
ggplot(mpg, aes(displ, hwy)) + geom_point()

ggplot(mpg) + geom_point(aes(displ, hwy))


# Tidy evaluation ----------------------------------------------------
# aes() automatically quotes all its arguments, so you need to use tidy
# evaluation to create wrappers around ggplot2 pipelines. The
# simplest case occurs when your wrapper takes dots:
scatter_by <- function(data, ...) {
  ggplot(data) + geom_point(aes(...))
}
scatter_by(mtcars, disp, drat)


# If your wrapper has a more specific interface with named arguments,
# you need the "embrace operator":
scatter_by <- function(data, x, y) {
  ggplot(data) + geom_point(aes({{ x }}, {{ y }}))
}
scatter_by(mtcars, disp, drat)


# Note that users of your wrapper can use their own functions in the
# quoted expressions and all will resolve as it should!
cut3 <- function(x) cut_number(x, 3)
scatter_by(mtcars, cut3(disp), drat)